Modeling & Conclusion

Models used

• Linear regression
• Multiple regression
• Random forest regression

Model specifications

• Response variable = aggregate happiness (sum of 11 different 5-scaled life questions on areas such as family and leisure time)
• Predictor variable = top eight predictor variables as measured by each variable’s mean decrease in gini coefficient (as shown in hypothesis 3)
  • Except for linear regression which only used age as predictor
• 80/20 split between training and testing sets

Model findings

• Linear regression MSPE: 25.25 units
• Multiple regression MSPE: 24.88 units
• Random forest regression MSPE: 23.57 units

Discussion

• Predictor variables clearly not good predictors - despite trying three different models, all the MSPEs are over half the range of the response variable
• Possible sources of bias:
  • 57.6% of original data was omitted due to null values
  • Measurement bias - survey was very long, respondents may have given inaccurate responses to finish quicker
  • Social desirability bias - respondents may have overreported happiness

Conclusion

• Different predictor variables will have to be surveyed to provide better prediction of happiness
• Findings reflect complex and abstract nature of happiness
• Possible future steps:
  • Longitudinal study so that inter-person differences in happiness can be normalized
  • Use friends/family reports of respondents’ happiness to counteract social desirability bias